Quality control is an essential component of parts manufacturing and can be regulated at statutory, administrative, and industry levels. For example, in the aerospace industry, in addition to government regulations, quality control procedures must follow the AS9100 system to comply with the industry standard. In particular, compliance with applicable rules generally requires a part manufacturing process to produce parts with characteristics, or features, that satisfy a set of requirements set down in a specification. The specification includes a nominal, or a target value, for the features. As producing a feature dimensioned precisely to the nominal value is statistically unlikely, the specification also includes a manufacturing tolerance, which is a range of acceptable variations from the nominal. The smallest value within the tolerance is called a lower specification limit. The highest value within the tolerance is called an upper specification limit. The ability of a manufacturing process to produce part features that are within the specification is referred to as the capability of the process.
Controlling process capability may not be sufficient to ensure usability of manufactured parts. Depending on a part, features of the part may gravitate towards the upper specification limit or the lower specification limit. For example, if a part includes a hole, the features of the part associated with the hole tend to be produced closer to the lower specification limit, which allows for the hole to be enlarged if necessary. On the other hand, if a part includes a concave surface, production of features of the part associated with the surface may gravitate towards the upper specification limit, which allows for the surface of the part to be reduced if necessary. As a result, the manufacturing processes may produce mating parts that are both within tolerance, but which are incompatible with each other. As a number of interacting parts grows, the problems caused by these incompatibilities accumulate. For example, aerospace parts manufacturing involves complex supply chains with large number of suppliers, small quantities of parts produced, and high requirements for precision in parts manufacturing. Building a commercial airplane may require on the order of 650,000 parts, and involve on the order of 20,000 people manufacturing the parts. As each part has on average ten features, there are 6,500,000 potential areas of conflict involved in putting the parts together to make the airplane. The incompatibilities between the parts require the parts to be either modified or replaced, increasing the cost and the time required to produce the airplane.
The situation is further exacerbated due to a lack of an easy way to see whether a particular parts manufacturer produces particular features of parts close to the nominal, or whether these features gravitate towards upper or lower specification limits. As a result, when a customer needs particular mating parts, the customer has no easy way of knowing whether the parts produced by different manufacturers will be compatible with each other.
Current technology is not sufficient to effectively address these challenges. For example, statistical process control (“SPC”) is a quality control method that uses statistical techniques to identify a cause for variation among part features produced using the same process. SPC involves periodically sampling of part features being manufactured, and calculating a mean of the measured samples. SPC further uses a control chart to visualize the variation among the parts. The mean is set as a centerline on the chart and three standard deviations away from the mean are shown as upper and lower control limits. While certain variation in output is inherent in any manufacturing process, SPC assumes that special causes for variation, such as a malfunction in a production machinery, exist when certain sampling patterns occur, such as a sampling point falling outside the upper or the lower control limits on the chart. The identification of the special variation allows the parts manufacturer to remove the cause for variation, and ideally, to control the manufacturing process. Thus, SPC allows to keep dimensions of features of parts within a certain distance from the mean, without bringing the features of the manufactured parts closer to the nominal. Furthermore, SPC is only effective when there is a small number of variables involved, and as the number of variables increases, the identification of the cause for the special variation can become impracticably difficult. Thus, in industries such as the aerospace industry, where there is a large number of variables involved in parts manufacturing and where outsourcing of part manufacturing and creation of vast supplies chains are common, the effectiveness of SPC is even more limited.
Therefore, there is a need for an efficient way to organize and present data regarding how close to the nominal are part features produced under certain circumstances.